Profiling of circulating microRNAs: from single biomarkers to re-wired networks

Abstract

The recent discovery that microRNAs (miRNAs) are present in the circulation sparked interest in their use as potential biomarkers. In this review, we will summarize the latest findings on circulating miRNAs and cardiovascular disease but also discuss analytical challenges. While research on circulating miRNAs is still in its infancy, high analytical standards in statistics and study design are a prerequisite to obtain robust data and avoid repeating the mistakes of the early genetic association studies. Otherwise, studies tend to get published because of their novelty despite low numbers, poorly matched cases and controls and no multivariate adjustment for conventional risk factors. Research on circulating miRNAs can only progress by bringing more statistical rigour to bear in this field and by evaluating changes of individual miRNAs in the context of the overall miRNA network. Such miRNA signatures may have better diagnostic and prognostic value.

Figure 1

Compartmentalization of circulating miRNAs. Circulating miRNAs are contained within vesicles (exosomes, microparticles, apoptotic bodies), in protein complexes (Ago2, NPM1) and in lipoprotein complexes (HDL, LDL complexes). Although various tissues contribute to the circulating miRNA pool, most miRNAs are probably derived from blood cells. In response to injury, such as AMI, cardiac-specific miRNAs, which are otherwise undetectable are released into the circulation.

Figure 2

Circulating levels of miR-126 across categories of normal glucose tolerance (NGT), impaired fasting glucose/impaired glucose tolerance (IFG/IGT) and manifest DM. Squares and lines indicate adjusted geometric means and 95% CIs [white squares: values adjusted for age and sex and black squares: values adjusted for age, sex, social status, family history of DM, body mass index, waist-to-hip ratio, smoking status, alcohol consumption (g/day), physical activity (sports index) and high-sensitivity C-reactive protein]. This analysis was performed in the entire study population (n= 822). Differences in miR-126 between categories of NGT, IFG/IGT and DM were compared with General Linear Models (GLM) and P-values are for trend. World Health Organization (WHO) definition of categories: normal glucose tolerance (fasting glucose < 110 mg/dL and 2 h glucose < 140 mg/dL), impaired fasting glucose/impaired glucose tolerance (110 mg/dL ≤ fasting glucose < 126 mg/dL, 140 mg/dL ≤ 2 h glucose < 200 mg/dL) and manifest DM. American Diabetes Association (ADA) definition of categories: normal glucose tolerance (fasting glucose < 100 mg/dL and 2 h glucose < 140 mg/dL), impaired fasting glucose/impaired glucose tolerance (100 mg/dL ≤ fasting glucose < 126 mg/dL, 140 mg/dL ≤ 2 h glucose < 200 mg/dL) and manifest DM (reproduced with permission from Zampetaki et al., Circ Res, 2010).

Figure 3

Circulating miRNA networks. Differential network structure between 13 miRNAs in controls and manifest DM. Nodes represent individual miRNAs and edges (links) represent the extent of expression similarity measured using the Context likelihood of relatedness algorithm. For each miRNA topological parameters including node degree, clustering coefficient, and eigenvector centrality can be systematically calculated. Node degree is defined as the total number of edges that are connected to a given miRNA. Clustering coefficient is the degree to which miRNAs tend to cluster together. Eigenvector centrality is a measure of miRNA importance, such that a particular miRNA receives a greater value if it is strongly correlated with other miRNAs that are themselves central to the network. Control (13 nodes, 25 links) and DM (13 nodes, 19 links) networks shared only 10 links. miR-126 occupied a central position within the network and the disease state was characterized by substantial edge rewiring, i.e. for miR-15a (reproduced with permission from Zampetaki et al., Circ Res, 2010).